PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Sci Total Environ. Author manuscript; available in PMC 2017 April 1.
Published in final edited form as:
PMCID: PMC4760902
NIHMSID: NIHMS753253

Household concentrations and personal exposure of PM2.5 among urban residents using different cooking fuels

Abstract

Exposure to PM2.5 is a leading environmental risk factor for many diseases and premature deaths, arousing growing public concerns. In this study, indoor and outdoor PM2.5 concentrations were investigated during the heating and non-heating seasons in an urban area in northwest China. Personal inhalation exposure levels among different age groups were evaluated, and the difference attributable to different cooking fuels including coal, gas and electricity, were discussed. The average concentrations of PM2.5 in the kitchen and the bedroom were 125±51 and 119±64 µg/m3 during the heating season, and 80±67 and 80±50 µg/m3 during the non-heating season, respectively. Indoor PM2.5, from indoor combustion sources but also outdoor penetration, contributed to about 75% of the total PM2.5 exposure. Much higher indoor concentrations and inhalation exposure levels were found in households using coal for cooking compared to those using gas and electricity. Changing from coal to gas or electricity for cooking could result in a reduction of PM2.5 in the kitchen by 40–70% and consequently lower inhalation exposure levels, especially for children and women.

Keywords: household air pollution, inhalation exposure, different cooking fuel, PM2.5

Graphical abstract

An external file that holds a picture, illustration, etc.
Object name is nihms753253f5.jpg

1. Introduction

Particulate matter with an aerodynamic less than 2.5 µm (PM2.5) is a major air pollutant that has been raising public concerns. A correlation between PM2.5 pollution and human morbidity and mortality due to cardiovascular and respiratory diseases has been widely documented in literature (Franklin et al., 2006; Kumar et al., 2008; Liao et al., 1999; Morris, 2001; Pope III et al., 2002). The latest Global Burden of Disease reported that exposure to ambient air pollution and household air pollution (HAP) from solid fuel use are leading environmental risk factors in many developing countries including China (Lim et al., 2012). Severe ambient air pollution in China during the last few years has been reported by some previous studies, most of which were done in relatively developed regions and megacities (Cheng et al., 2013; Ma et al., 2014; Tao et al., 2014; Kong et al., 2013; Wang et al., 2014). The ambient PM2.5 levels are frequently found to be several times that of the World Health Organization standard (WHO, 2006) in most of these areas, and both primary emission sources and secondary formation are responsible for high pollution episodes (Huang et al., 2015).

Aside from outdoor pollution, indoor PM2.5 pollution is also a problem that cannot be ignored. In many countries, most people spend 80~90% of their time indoors (Delgado-Saborit et al., 2011; Koistinen et al., 2001; Scapellato et al., 2009). Because of the low burning efficiency of residential solid fuel combustion (Shen et al., 2010, 2012a), many studies found higher indoor PM2.5 concentrations compared to that in outdoor air, especially in rural areas. In fact, HAP from solid fuel combustion is considered to be responsible for more than one million premature deaths in China. Different from the heavy focus on outdoor PM2.5 pollution, few studies so far have investigated HAP in China, in either urban or rural regions (Chau et al., 2002; Mestl et al., 2006; Shen et al., 2014a; Wang et al., 2008) and considered the pollution characteristics by season and/or fuel type. The latest Chinese Environmental Exposure-Related Human Activity Patterns Survey (CEERHAPS) showed that many different sources of energies like gas, electricity, and traditional solid fuels including biomass and coals, have been used for household cooking and heating nowadays, and the pattern varies notably between urban and rural areas (Duan et al., 2014). The use of clean fuels in the resident sector is expected to lower air pollution (Shen et al., 2012b, 2014b) and hence, to benefit human health. However, few studies so far quantified indoor air pollution attributable to different fuel use in households in China (Fischer and Koshland, 2007; Zhong et al., 2012).

The main objective of this study is to characterize household concentrations and personal inhalation exposure levels of PM2.5 during the heating and non-heating seasons among urban residents located in northwest China. Differences in household concentrations and inhalation exposure attributable to different cooking fuels were discussed. The results can be used as a reference to establish a model of PM2.5 exposure based on household cooking fuel type that would be informative for future epidemiologic studies. The study could also provide important information for future clean fuel intervention programs by quantifying probable changes in indoor PM2.5 concentration and inhalation exposure when switching from traditional solid fuels to cleaner household fuels.

2. Materials and methods

2.1 Study area and indoor/outdoor sampling

The study is conducted in Lanzhou (36°N; 103°40′E), northwest China (Figure S1). Usually, the heating season in Lanzhou lasts for 5 months, from November to March. A total of 53 and 54 households were investigated during the heating (February 27th ~ March 9th, 2013) and non-heating seasons (September 14th ~ September 24th, 2013), respectively.

Indoor PM2.5 samples were taken from two locations, the kitchen and the bedroom, in each household using air sampling pumps (LP-5, BUCK, USA) with a PM2.5 cutter connected at a flow rate of 2 L/min with a sampling duration of 24 hours. The flow rate of the sampling device was calibrated using a soap bubble flow corrector. The PM2.5 samples were collected on Teflon membrane filters (PALL, USA), with a diameter of 37 mm and an aperture of 2.0 µm. The Teflon filters were preheated at 35 ± 5°C for 4 hours, equilibrated in a dark desiccator for 24 hours, weighed using a hundred thousandth electronic balance (0.01 mg, Mettler-Toledo, Switzerland), and then stored in polytetrafluoroethylene Ziploc bags. Sample sites were closed off at the air-vent and the wall. In principle, the height of the sample point was consistent with the breathing zone and the range of relative heights were 1.0 ~ 1.5 m. When the instruments were in place, each household member was advised to maintain their daily habits and to make timely contact with the person responsible for the sampling if any problems occurred with the instruments. The necessary data including volumes and sampling durations were recorded at the end of each sampling period. The particle-loaded filters were removed carefully from the PM2.5 cutter, equilibrated in a desiccator for 24 hours, and then gravimetrically weighed (0.01 mg, Mettler-Toledo, Switzerland). Based on the sampling volume of about 2.88 m3 (2.0 L/min lasting for 24 hours), the PM2.5 concentration is present at a precision of 1 microgram per cubic meter.

Outdoor air samples were collected by using intelligent constant flow sampling pumps (KC-120, Qingdao, China) with a flow rate of 100 L/min during the heating and non-heating seasons, same as that in the indoor air sampling campaign. The quartz membrane filters (diameter = 90 mm, PALL) were wrapped in silver foil, baked at 400°C for 4 hours in a muffle furnace and then equilibrated in a desiccator for 24 hours. The treated filters were weighed and stored in the same way as the Teflon filters. During the sampling period, all of the sites were well ventilated. The weather was mainly cloudy or sunny, and there was no severe weather such as gales, rainstorms, or sand storms. Since ambient air pollution of fine PM2.5 in an urban scale is often homogeneous, we only collected 18 outdoor samples (7 during the heating season and 11 during the non-heating season).

2.2 Data analysis

The PM2.5 exposure for different gender/age groups was estimated using equation (1):

Ei=124tjqj,
(1)

where Ei is the exposure level of PM2.5 pollution (µg/m3); tj is the time spent by population groups in microenvironment j; and qj is the 24-h PM2.5 average concentration in microenvironment j. The microenvironments investigated here included the kitchen, bedroom, and outdoor. The average outdoor PM2.5 concentration, calculated from all outdoor sampling sites, was used for different age groups using different fuels. Measured indoor PM2.5 concentration was used with respect to the indoor microenvironments and fuel types. Due to small differences in climate, social economy, and living habits, the time activity patterns of Shanxi province (Mestl et al., 2006) (also in northwest China) were used in this study (Table S1). The results were expressed as means and standard deviations (x ± s).

A structured questionnaire was used to collect household information, including the type of cooking fuel (coal, gas or electricity), space heating approach (central heating system or individual household heating stoves), food preparation methods (boiled, light-frying, deep frying etc), cooking frequency, smokers present in family, and so on. The information was used in a mixed effect regression model to identify key factors affecting indoor PM2.5 level. SPSS 20.0 was used for the data analysis. A significance level of 0.05 was adopted. The graphs presented here were drawn using Origin8.5 or Excel.

3. Results and Discussion

3.1 Indoor and outdoor PM2.5 concentrations

The PM2.5 concentration in the kitchen and the bedroom are summarized in Table 1. There were no significant differences in the PM2.5 concentrations found between the kitchen and the bedroom during either season (p>0.05), but as expected, the PM2.5 levels were significantly higher during the heating than those during the non-heating season (p<0.05). A significantly positive correlation was found between the kitchen PM2.5 concentration and the bedroom PM2.5 concentration (Figure 1), suggesting that they may be affected by the same sources, or a well air exchange between the kitchen and the bedroom.

Figure 1
Relationship between PM2.5 concentration in the bedroom and that in the kitchen measured using stationary samples during the heating and non-heating seasons. The scales are in the logarithmic scale. A 1:1 line is shown.
Table 1
The PM2.5 concentrations (µg/m3) in the kitchen and the bedroom of the investigated households during the heating and non-heating sampling periods.

The average outdoor daily PM2.5 concentration was 80±49 µg/m3 (40–166 µg/m3 as range) during the non-heating season and much higher during the heating period with a mean and standard derivation of 328±104 µg/m3 (225–530 µg/m3 as range). The Coefficients of Variation (COVs, defined as standard derivation divided by the mean) were 32% and 61% during the heating and non-heating sampling periods, respectively. Since outdoor samples were collected from different sites in the studied urban area, the variance in 30–60% may suggest that the outdoor PM2.5 pollution at the urban scale is often homogeneous and support our current sampling approach-that is paired bedroom-kitchen samples in each household but selected outdoor PM2.5 samples in these two seasons (see method section).

Indoor PM2.5 concentrations, in both the kitchen and the bedroom, were not significantly different from the outdoor PM2.5 level during the non-heating season (p>0.05). However, during the heating period, outdoor PM2.5 was significantly higher than the kitchen and bedroom PM2.5 concentrations (Figure 2). One main reason is a distinct indoor-outdoor air exchange between the heating and non-heating periods. During the cold heating seasons, most residents close windows while in the non-heating periods, people may like to keep windows open for a relatively longer time. Results from questionnaires confirmed that the average durations of “how long windows were open per day” were 7.7 hours during the heating season, significantly lower than that of 15.8 during the non-heating season.

Figure 2
Comparion of indoor (kitchen and bedroom) and outdoor PM2.5 concentrations during the non-heating and the heating sampling periods. Data shown are means and standard derivations.

3.2 Inhalation exposure and indoor and outdoor contributions

Based on the measured concentrations and fractions of the day spent in each microenvironment (method section), personal inhalation exposure levels were estimated for different groups of children under 14 years (same activity pattern for boys and girls), males and the females aged 15–64 years, and males and the females older than 65 years during the heating and non-heating seasons. During the heating season, the average PM2.5 exposures for children aged 0–14 years were 58±22 µg/m3. The concentrations were 54±20 and 64±22 µg/m3 for males and females aged 15–64 years, respectively, and 54±20 and 59 ±21 µg/m3 for males and females older than 64 years, respectively. During the non-heating season, the corresponding values were 38±18 µg/m3 for children under 14 years, 34±16 and 41±19 µg/m3 for males and females aged 15–64 years, and 34±16 and 38±17 µg/m3 for elder males and females above 64 years, respectively. The average PM2.5 exposure for each category of people evaluated during the heating season was significantly higher than for those evaluated during the non-heating season.

Relative contributions of PM2.5 in different microenvironments differed among the population groups. Overall, indoor PM2.5 contributed largely to the overall PM2.5 exposure. During the heating season, the contributions of indoor PM2.5 exposure for children under 14 years, males aged older than 15, females aged 15–64 and females older than 64 were 76.6%, 74.5%, 78.6%, and 76.7% of the total, respectively. The corresponding contributions of outdoor PM2.5 exposure were only about 21.4%–25.5% for the studied population groups. During the non-heating season, 79.8%, 77.8%, 81.4%, and 79.8% of the total PM2.5 exposure were attributable to indoor PM2.5 for the four groups. The contribution of PM2.5 in outdoor air to total inhalation exposure appeared to increase slightly during the heating seasons compared to that in the non-heating seasons, though the outdoor PM2.5 concentration was much higher in the heating season. As people spent a smaller fraction of the day outdoors, the increase was smaller in total inhalation exposure compared to the increase in outdoor air concentration.

It is necessary to note that the indoor PM2.5 here was from indoor combustion sources but also partly from outdoor penetration, especially in the heavy pollution period during the heating seasons. And, vice versa, indoor combustion can contribute to outdoor PM2.5 at different degrees varying on sampling periods and sites. The estimated relative contributions of indoor and outdoor exposure did not indicate contributions from indoor and outdoor PM2.5 sources. The generalization of this result should be taken with caution.

The contribution of PM2.5 in the bedroom was the highest compared to that in the kitchen and in outdoor air. This is because of relatively longer duration in the bedroom that that in the kitchen and outdoor. The PM2.5 in the bedroom may come from indoor combustion sources like fuel combustion in the kitchen, and also outdoor penetration. As mentioned above, a significantly positive correlation was found between kitchen PM2.5 and bedroom PM2.5, suggesting common sources of these particles; however it is impossible to separate or identify the common sources of indoor fuel combustions and/or outdoor penetration based on only PM mass concentration data here. Again, the contributions here are calculated for PM2.5 in different microenvironments to the total overall PM2.5 exposure, rather than for sources in indoor and/or outdoor areas. However, it would be highly interesting and meaningful to do this. The contribution of PM2.5 in the kitchen was lower for children under 14 years, but higher for females aged 15–64 years. That is again mainly due to different times spent in the kitchen among different population groups.

3.3 Difference attributable to household cooking fuels

Household fuel use is one important source of indoor PM2.5 pollution. Among the investigated households, near 75% of households used gas fuels like liquefied petroleum gas (LPG) and natural gas for daily cooking, while about 13% used electricity for cooking with the remaining using coal as the main cooking fuel. The fuel use profile was very similar to that found in the last and also the first CEERHAPS (Duan et al., 2014). Variances in indoor levels of air pollutants in the use of different fuels are often expected. As shown in Figure 3, during the heating season, the average PM2.5 concentrations in kitchens when coals were used (204±50 µg/m3) were significantly higher than in those using gas (114±39 µg/m3) and in those using electricity (107±43 µg/m3) (p <0.01). The average PM2.5 concentrations in the bedrooms of homes using coal, electricity, and gas were 159±60 µg/m3, 139±99 µg/m3, and 109±57 µg/m3, respectively, with a statistically significant high daily PM2.5 concentration in the bedroom of homes using coals (p <0.01).

Figure 3
Comparison of daily PM2.5 concentrations in the kitchen and bedroom of households using different cooking fuels during the heating and non-heating seasons. Data shown are means and standard derivations. The sample size in each sub-group is listed.

During the non-heating season, the average PM2.5 concentrations in the kitchen using coal, gas, and electricity were 213±89 µg/m3, 65±42 µg/m3, and 55±35 µg/m3, respectively (Table 2), and the differences were again statistically significant (p <0.05). For the PM2.5 in the bedroom, the daily average in homes using coal, gas, and electricity were 102±65 µg/m3, 79±49 µg/m3, and 68±41 µg/m3, respectively. High indoor contamination levels of PM2.5 when coals were used for cooking were also revealed, however, the differences were not statistically significant (p>0.05), which could be probably due to good ventilation conditions and relatively longer ventilation times during the non-heating season.

Table 2
Daily PM2.5 concentrations (µg/m3) in the kitchen and the bedroom when different energies used for cooking during the heating and non-heating sampling periods. During the heating period, there are two different heating ways: central heating and ...

Such a difference in indoor air would result in consequent variations in daily PM2.5 exposure levels of the population using different fuels. Higher inhalation exposure concentration were observed among residents using coals as the main cooking fuel, followed by those using electricity and gas (Figure 4). The tendency was same in the heating and non-heating sampling periods. Consequently, calculated contributions of PM2.5 in the kitchen to the overall inhalation exposure vary among groups using different household cooking fuels. The percentages increased among groups who used coals for cooking compared to among those using gas and electricity for daily cooking. The contributions of indoor PM2.5 to the overall total exposure were approximately 4~7-fold more than that of outdoor PM2.5 (4.5-, 4.0-, 5.3-, and 4.6-fold more during the heating season and 5.6-, 5.1-, 7.4-, and 6.2-fold more during the non-heating season for the four groups, respectively) when coals were used.

Figure 4
Estimaetd daily PM2.5 inhalation exposure for differernt age groups (0–14, 14–64 and >65 for the male and the female) using different hosuehold cooking energies (gas, electricity and coal).

3.4 Difference attributable to other influencing factors

Cooking frequency

Aside from the fuel types used for cooking, cooking methods and frequency may affect indoor air quality as well. Due to similar habits among residents in the region, light-frying and/or boiling are typical food preparation ways in most families in their daily lives. The average kitchen PM2.5 concentrations were 92±33 µg/m3, 115±35 µg/m3, 131±57 µg/m3, and 135±54 µg/m3 when the daily cooking frequency was 0, 1, 2 and 3, respectively. There was an increased tendency, but statistically not significant.

Household heating approach

In cold seasons, space heating is required in Lanzhou, same as in most Chinese households located in north region. Two distinct heating ways are most commonly in the urban area of Lanzhou: heating supplied by the central heating system or individual household heating by installing coal stoves. The average daily PM2.5 concentrations in the kitchen and bedroom of households using individual coal stove for heating were 167±40 µg/m3 and 177±85 µg/m3, respectively. The concentrations were much higher than those in households covered by the central heating system (109±45 µg/m3 and 98±38 µg/m3, respectively) (Table 2). The burning efficiency of residential coal burning in individual household heating stoves is usually low, and hence produces high emissions of air pollutants from the incomplete combustion process. Therefore, the use of low efficiency individual household heating stove may directly result in severe indoor air pollution.

Smokers in household

Smoking is one important behavior affecting indoor air quality. The daily kitchen and bedroom PM2.5 concentrations were 137±49 µg/m3 and 118±51 µg/m3, respectively when there are smokers present. The levels were apparently higher than 125±70 µg/m3 and 115±62 µg/m3 when no smokers were reported at the sampling time, but statistically not significant (p>0.05). This could be due to large variances in measured PM2.5 concentrations and interacted impacts with other influencing factors.

Information for many other factors like household layout, household area, and separate kitchens, are also collected from questionnaires and analyzed by using the multivariable linear regression models. The results showed that the sampling period (heating vs non-heating seasons) and cooking fuels (gas, electricity or coal) are the two most significant factors, while the influences of others are statistically insignificant. It is necessary to note that some quantitative information like hours of open windows per day, duration in the kitchen area and cooking times, are all self-reported. Self-reported errors cannot be avoided, causing potential uncertainties in the analysis results of these factors.

3.5 Discussion and limitations

Exposure to PM2.5 has been identified as a leading environmental risk factor for many diseases and premature deaths globally, especially in developing countries like China. So far, PM2.5 is not regulated in the Chinese Indoor Air Quality Standard (GB/T 18883-2002). WHO air quality guidelines of daily PM2.5 concentration is 25 µg/m3 (WHO, 2006) and this is also the guideline applicable to indoor environments since there is no convincing evidence of a difference in the hazardous nature of particles from indoor sources as compared to in those from outdoor sources (WHO, 2006, 2010). Therefore, results from the present study indicated that daily indoor PM2.5 levels exceeded the standard limit nearly all days during the heating season, and over 85% of days during the non-heating season. Overall, the indoor PM2.5 concentrations during the heating and non-heating seasons were 122±58 µg/m3 and 80±59 µg/m3, respectively, which were 4.9- and 3.2-fold of the limit of 25 µg/m3, respectively. In the new Chinese ambient air quality standard, the limit of daily PM2.5 level is 75 µg/m3. In comparison, outdoor PM2.5 pollution in the studied area clearly exceeded the national air quality standard in both heating and non-heating seasons. Nearly 4.7-fold of the standard could be found in the heavily polluted heating season. Such a high pollution level would indicate more serious detrimental impacts on human health.

Female aged 15–64 years had the highest PM2.5 inhalation exposure concentration. Shimada and Matsuoka (2011) analyzed the PM2.5 exposure level in four Asian countries and found that the PM2.5 exposure level was highest for unemployed women aged 35–64 years. Balakrishnan et.al (2004) investigated the indoor PM concentrations over a 24-hour period in 412 households using bio-fuels in the Andrah Pradesh, and also found that PM exposure levels for the female between 5–40 years were significantly higher than those for the male and younger children. Women in this age group were most likely to be involved in cooking activities (Balakrishnan et al., 2004). The PM2.5 exposure concentrations were generally higher among children aged 0–14 years and women when compared with the exposure levels among males older than 14 years, indicating that children and women could be more vulnerable to air pollution than men. Therefore, reducing PM2.5 levels is of high priority for protecting females and children.

As most residents spend longer time indoors rather than outdoors, severe indoor air pollution often results in high inhalation exposure and consequent high risks for many respiratory and cardiovascular diseases. Though varying among different age groups, the indoor PM2.5 contributed to nearly 75% of the total PM2.5 inhalation exposure. Severe outdoor PM2.5 pollution in heating seasons may increase the relative contribution of outdoor PM2.5 exposure to the total. However, due to relatively short fractions of the day spent outdoors, the increase might be small. In addition, increased outdoor PM2.5 would also result in elevated indoor PM2.5 levels due to outdoor penetration, and hence show higher indoor contributions since we now calculated the contributions of indoor and outdoor PM2.5 exposure, instead of individual contributions of indoor and outdoor sources. Chau et.al (2012) investigated the exposure concentrations of PM10 for different groups in Hong Kong and reported that indoor microenvironments contributed to 54.7%, 38.7%, and 32.1% of the PM10 exposure for the juvenile, the adult, and the elderly, respectively. Likewise, Wang et.al (2008) estimated that PM10 in indoor microenvironments contributed to 41%, 54%, and 80% of the PM10 exposure of the juvenile, the adult, and the elderly, respectively. Therefore, the indoor environment makes an important contribution to the overall particulate matter exposure for urban residents, even though the relative contribution percents display a temporal and regional variation. In the high pollution episode, the public is often guided to protect themselves by staying at home and/or closing windows. However, due to a much larger fraction of the day spent indoors, it may be more necessary to conduct effective actions to reduce indoor pollution of PM2.5, as well as other pollutants, to lower exposure level and protect human health.

Household solid fuel use is one important source of PM2.5, as well as some other air pollutants. The use of traditional solid fuels like coals for cooking leads to significantly high HAP and personal inhalation exposure as compared to the use of relatively cleaner gas and electricity. The kitchen PM2.5 level may decrease by about 40–70% if a change is made from the use of coals to gas and/or electricity. The reduction in the bedroom PM2.5 was around 30%. Consequent decrease in daily PM2.5 exposure could be also expected. Differences in indoor air pollution attributable to different fuel use have also been evident in some previous studies. For example, Siddiqui et al. (2009) monitored 51 kitchens using wood and 44 kitchens using LPG, and found that the daily average PM2.5 concentration in the kitchen using wood (2740 µg/m3) was significantly higher than in those using LPG (380 µg/m3) (p <0.001). Brauer et al. (1995) investigated 22 kitchens in rural areas in Mexico and reported that the average PM2.5 concentration in the kitchen using biomass was 555 µg/m3, which was significantly (p <0.05) higher than that in the kitchen using LPG (69 µg/m3). These studies confirm that the use of traditional solid fuels like biomass and coal may lead to high indoor pollution and subsequent higher exposure concentration, while the use of clean fuels like gas and electricity could lower indoor air pollution significantly. But, it is very important to note that even in households when gas and electricity were used for cooking, the indoor PM2.5 might be still very high like in this study, in which they were still above the WHO guideline of 25 µg/m3, indicating more effective efforts and pollution control actions are needed in the future to improve indoor air quality.

We acknowledged that one important limitation of this study is that personal inhalation exposure was estimated based on the concentrations measured from stationary samplers and literature reported personal activity patterns. The use of personal carried samplers is more appropriate for the evaluation of inhalation exposure, and the technology has been adopted in some field studies. However, because of relatively high costs and high cooperation of local residents, there are often more difficulties in the field use of personal carried samplers. Another limitation of this study is that only three microenvironments including the kitchen, bedroom and the outdoors are analyzed here. People may stay at other microenvironments The contributions of PM2.5 in different microenvironments to the overall total PM2.5 exposure in the present study did not indicate contributions from indoor and outdoor sources. Indoor combustion sources may contribute to outdoor PM2.5 and vice versa. Therefore, future studies to identify potential sources of indoor and outdoor PM2.5 and attribute contributions to different source types would be helpful for policy makers to develop effective pollution control countermeasures. It is also noted that in the present study, only external inhalation exposure was characterized, while the risks and health outcomes due to PM2.5 inhalation exposure are not studied here. Further works would be interesting, particularly those taking dose-effect relationship and variances in individual susceptibility into account.

4. Conclusions

There are significant seasonal variations in indoor PM2.5 levels among different microenvironments. The average PM2.5 concentrations in the kitchen and the bedroom were 125±51 µg/m3 and 119±64 µg/m3 during the heating season and 80±67 µg/m3 and 80±50 µg/m3 during the non-heating season, respectively, without significant differences between the kitchen and the bedroom in both seasons. The PM2.5 concentration in the bedroom positively correlated with that in the kitchen, indicating that they are from or affected by similar indoor and/or outdoor sources. Overall, the PM2.5 exposure in indoor air contributed up to about 75% of total inhalation exposure. However, it is necessary to note that PM2.5 in indoor air may come from outdoor air, especially in heavily polluted heating seasons, and vice versa. Therefore, the result should not be simply generalized and must be used with caution in future studies. During the heating season, the daily average inhalation exposure of PM2.5 were 58 µg/m3, 54 µg/m3, 64 µg/m3, and 59 µg/m3 for children at 0–14 years, males older than 15, females aged 15–64, and females older than 64, respectively. The corresponding results were 38 µg/m3, 34 µg/m3, 41 µg/m3, and 38 µg/m3, respectively, during the non-heating sampling period. Generally, the PM2.5 exposure in grown females was the highest and in grown/elderly males the lowest.

The indoor PM2.5 concentrations in households using coal were significantly higher than in those using gas or electricity. And consequently, the exposure concentration in the group using coal for cooking was significantly higher than those in groups using gas and/or electricity. The contribution of the indoor microenvironments using coal for daily cooking to the overall PM2.5 exposure was 4 ~ 7-fold higher than that of the outdoor environment. Changing from coal to gas or electricity would have a reduction of PM2.5 in the kitchen by about 40–70% and result subsequent reductions in personal inhalation exposure levels.

Highlights

  • Comparison and seasonal differences in indoor and outdoor PM2.5
  • Estimate personal inhalation exposure and indoor/outdoor contributions
  • Distinct indoor PM2.5 levels attributable to different cooking fuels
  • Use of coal for cooking lead to higher indoor pollution and exposure level
  • Indoor PM2.5 exposure contributed to near 75% of total inhalation exposure

Supplementary Material

Acknowledgments

This research was supported by the Ministry of Environmental Protection of China (201109064), the State Key Laboratory of Environmental Criteria and Risk Assessment (SKLECRA2015OFP02 and SKLECRA2013OFP005) and the U.S. National Institutes of Health (K02HD70324). We also sincerely thank Mr. Zhihan Zou (McGill University) for his proof reading for English language.

Appendix

Information about the location of the sampling site and activity patterns for different age/gender groups is provided in the appendix and available online.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The authors declare no competing financial interests.

References

  • Balakrishnan K, Sambandam S, Ramaswamy P, Mehta S, Smith KR. Exposure assessment for respirable particulates associated with household fuel use in rural districts of Andhra Pradesh, India. J Expo Sci Environ Epid. 2004;14:S14–S25. [PubMed]
  • Brauer M, Bartlett K, Regalado-Pineda J, Perez-Padilla R. Assessment of Particulate Concentrations from Domestic Biomass Combustion in Rural Mexico. Environ Sci Technol. 1995;30:104–109.
  • Cao J, Lee S, Chow J, Cheng Y, Ho K, Fung K, Liu S, Watson J. Indoor/outdoor relationships for PM2. 5 and associated carbonaceous pollutants at residential homes in Hong Kong–case study. Indoor Air. 2005;15:197–204. [PubMed]
  • Chau C, Tu EY, Chan D, Burnett J. Estimating the total exposure to air pollutants for different population age groups in Hong Kong. Environ Int. 2002;27:617–630. [PubMed]
  • Cheng S, Lang J, Zhou Y, Han L, Wang G, Chen D. A new monitoring-simulation-source apportionment approach for investigating the vehicular emission contribution to the PM2.5 pollution in Beijing, China. Atmos Environ. 2013;79:308–316.
  • Delgado-Saborit JM, Aquilina NJ, Meddings C, Baker S, Harrison RM. Relationship of personal exposure to volatile organic compounds to home, work and fixed site outdoor concentrations. Sci Total Environ. 2011;409:478–488. [PubMed]
  • Duan X, Jiang Y, Wang B, Zhao X, Shen G, Cao S, et al. Household fuel use for cooking and heating in China: results from the first Chinese Environmental Exposure Related Human Activity Patterns Survey (CEERHAPs) Applied Energy. 2014;136:692–703.
  • Fisher S, Koshland C. Daily and peak 1 h indoor air pollution and driving factors in a rural Chinese village. Environ Sci Technol. 2007;41:3121–3126. [PubMed]
  • Franklin M, Zeka A, Schwartz J. Association between PM2. 5 and all-cause and specific-cause mortality in 27 US communities. J Expo Sci Environ Epid. 2006;17:279–287. [PubMed]
  • Koistinen KJ, Hänninen O, Rotko T, Edwards RD, Moschandreas D, Jantunen MJ. Behavioral and environmental determinants of personal exposures to PM2.5 in EXPOLIS – Helsinki, Finland. Atmos Environ. 2001;35:2473–2481.
  • Kong S, Ji Y, Liu L, Chen L, Zhao X, Wang J, et al. Spatial and temporal variation of phthalic acid esters (PAEs) in atmospheric PM10 and PM2.5 and the influence of ambient temperature in Tianjin, China. Atmos Environ. 2013;74:199–208.
  • Kumar R, Nagar JK, Raj N, Kumar P, Kushwah AS, Meena M, et al. Impact of domestic air pollution from cooking fuel on respiratory allergies in children in India. Asian Pac J Allergy. 2008;26:213–222. [PubMed]
  • Huang R, Zhang Y, Bozzetti C, Ho K, Cao J, Han Y, et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature. 2014;514:218–222. [PubMed]
  • Liao D, Creason J, Shy C, Williams R, Watts R, Zweidinger R. Daily variation of particulate air pollution and poor cardiac autonomic control in the elderly. Environ Health Perspect. 1999;107:521–525. [PMC free article] [PubMed]
  • Lim S, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010. a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2224–2260. [PMC free article] [PubMed]
  • Ma J, Chen LL, Guo Y, Wu Q, Yang M, Wu MH, et al. Phthalate diesters in Airborne PM2.5 and PM10 in a suburban area of Shanghai: Seasonal distribution and risk assessment. Sci Total Environ. 2014;497:467–474. [PubMed]
  • Mestl SHE, Aunan K, Seip HM. Potential health benefit of reducing household solid fuel use in Shanxi province, China. Sci Total Environ. 2006;372:120–132. [PubMed]
  • Morris RD. Airborne particulates and hospital admissions for cardiovascular disease: a quantitative review of the evidence. Environ Health Perspect. 2001;109:495–500. [PMC free article] [PubMed]
  • Pope CA, III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA. 2002;287:1132–1141. [PMC free article] [PubMed]
  • Scapellato ML, Canova C, De Simone A, Carrieri M, Maestrelli P, Simonato L, et al. Personal PM10 exposure in asthmatic adults in Padova, Italy: seasonal variability and factors affecting individual concentrations of particulate matter. Int J of Hyg Environ Heal. 2009;212:626–636. [PubMed]
  • Shimada Y, Matsuoka Y. Analysis of indoor PM2.5 exposure in Asian countries using time use survey. Sci Total Environ. 2011;409:5243–5252. [PubMed]
  • Siddiqui A, Lee K, Bennett D, Yang X, Brown K, Bhutta Z, et al. Indoor carbon monoxide and PM2.5 concentrations by cooking fuels in Pakistan. Indoor Air. 2009;19:75–82. [PubMed]
  • Shen GF, Yang YF, Wang W, Tao S, Zhu C, Min YJ, et al. Emission factors of particulate matter and elemental carbon for crop residues and coals burned in typical household stoves in China. Environ Sci Technol. 2010;44:7157–7162. [PMC free article] [PubMed]
  • Shen GF, Wei SY, Wei W, Zhang YY, Min YJ, Wang B, et al. Emission factors, size distributions and emission inventories of carbonaceous particulate matter from residential wood combustion in rural China. Environ Sci Technol. 2012a;46:4207–4214. [PMC free article] [PubMed]
  • Shen GF, Tao S, Wei SY, Zhang YY, Wang R, Wang B, et al. Reductions in emissions of carbonaceous particulate matter and polycyclic aromatic hydrocarbons from combustion of biomass pellets in comparison with raw fuel burning. Environ Sci Technol. 2012b;46:6409–6416. [PMC free article] [PubMed]
  • Shen GF, Zhang YY, Wei SY, Chen Y, Yang C, Lin P, et al. Indoor/outdoor pollution level and personal inhalation exposure of polycyclic aromatic hydrocarbons through biomass fuelled cooking. Air Qual Atmos Health. 2014a;7:449–458.
  • Shen GF, Xue M. Comparison of carbon monoxide and particulate matter emissions from residential burnings of pelletized biofuels and traditional solid fuels. Energy Fuels. 2014b;28:3933–3939.
  • Tao J, Zhang L, Ho K, Zhang R, Lin Z, Zhang Z, et al. Impact of PM2.5 chemical compositions on aerosol light scattering in Guangzhou — the largest megacity in South China. Atmos Res. 2014;135:48–58.
  • Wang D, Hu J, Xu Y, Lv D, Xie X, Kleeman M, et al. Source contributions to primary and secondary inorganic particulate matter during a severe wintertime PM2.5 pollution episode in Xi'an, China. Atmos Environ. 2014;97:182–194.
  • World Health Organization. World Health Organization; 2006. Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide.
  • Wang S, Zhao Y, Chen G, Wang F, Aunan K, Hao J. Assessment of population exposure to particulate matter pollution in Chongqing, China. Environ Pollut. 2008;153:247–256. [PubMed]
  • Zhong J, Ding J, Su Y, Shen G, Yang Y, Wang C, et al. Carbonaceous particulate matter air pollution and human exposure from indoor biomass burning practices. Environ Eng Sci. 2012;29:1038–1045.